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At the forefront of intelligent vehicle technologies are vehicle-to-vehicle communication (V2V) and vehicle-infrastructure integration (VII). Their capabilities can be added to currently-available systems, such as adaptive cruise control (ACC), to drastically decrease the number and severity of collisions, to ease traffic flow, and to consequently improve fuel efficiency and environmental friendliness. There has been extensive government, industry, and academic involvement in developing these technologies. This paper explores the capabilities and challenges of vehicle-based technology and examines ways that policymakers can foster implementation at the federal, state, and local levels.

Traffic congestion has become a major problem in the United States. Advanced technologies, referred to as Intelligent Vehicle/Highway Systems (IVHS), offer the best solution. IVHS allow communication between vehicle and highway. IVHS include Advanced Traffic Management Systems, Advanced Driver Information Systems, Commercial Vehicle Operations, and Automated Vehicle Control Systems. IVHS offer to reduce congestion, enhance safety, increase productivity, and improve U.S. competitiveness. IVHS development programs are underway in Europe and Japan. No national IVHS program exists in the United States. Three questions are asked. Is there need for a national IVHS program? Which system is intelligent, the vehicle or the highway? Which system comes first, the vehicle or the highway? Public and private sector cooperation is needed. The federal government will coordinate efforts between state and local governments and private industry.

The Intelligent Ground Vehicle Competition (IGVC) is a multidisciplinary exercise in product realization for college engineering students. They design, build, and compete with autonomous vehicles in events ranging from lane following, obstacle avoidance, platooning, to Global Positioning System (GPS) navigation. Technologies involved include electronic controls, computer-based vision systems, object detection, rangefinding, and global positioning. The real world applications are in intelligent transportation systems, the military, and manufacturing automation. Students have been creative and have learned a great deal. Industry recruiters have been highly supportive.

The Corporate Research of Infineon Technologies AG developed an intelligent vision system, which fulfills the requirement of a programmable, compact and cheap solution with high computational power. The core of our vision system is a processor called VIP (Vision Instruction Processor). A prototype is available and executes 53 GOps at 120 MHz. Furthermore a CMOS-camera was developed. The next design in 0.1 μm CMOS-technology (ready in 2004) allows the integration of camera, VIP and memory on an area of 60mm2. Prototypes in the field of driver assistance were developed. Performance results will be presented.

Today, commercially available drones have limited use-cases in the rapidly evolving community. However, with advances in drone and software technology, it is possible to utilize these aerial machines to solve problems in a variety of industries such as mining, medical, construction, and law enforcement. For example, in order to reduce time of investigation, Indiana State Police are currently utilizing ad-hoc commercial drones to reconstruct crash scenes for insurance and legal purposes. In this paper, we illustrate how to effectively integrate drones for in-vehicle services and real-time prediction for automotive applications. In order to accomplish this, we first integrate simpler controls such as voice-commands to control the drone from the vehicle. Next, we build smart prediction software that monitors vehicle behavior and reacts in real-time to collisions.

Warning lights and beacons on service vehicles such as maintenance trucks, tow trucks, utility service vehicles and delivery vehicles are an important line of defense for the workers who operate them. These flashing lights can also contribute to visual chaos making it difficult to navigate through a work zone location. Research on the flashing configuration and spatial and temporal coordination of warning lights that could adapt to ambient conditions and situations is described, leading to recommendations for preliminary performance specifications

Due to alternate local heating and cooling (Thermal cycles) during welding, complex stresses occur and so residual stresses and angular distortion occur after welding. The residual stresses and angular distortion present in the welded components cause internal cracks and mismatching of welded automobile structures. Also the tensile residual stresses in area near the weldment may cause fractures under certain conditions whereas compressive residual stresses in the base plate may reduce buckling strength of structural members. Hence a welding system which is capable of predicting and acting according to it is the need of the hour. It is difficult to obtain a more reliable complete analytical solution to predict angular distortion and maximum residual stresses for a wide range of welding processes, materials and process parameters.

This paper introduces intelligent headrest using DC motor, photo sensor, and piezo sensor. Intelligent headrest which follows driver''s head during driving can protect driver''s neck efficiently when the vehicle is crashed from rear direction. This paper, we propose an intelligent method of headrest movement. For this method, at first, we had to obtain the optimum information that is the gap between driver''s head and headrest from 10 tested people who included beginner and expert drivers. At second, we developed new control method using this data. As it were, if driver''s head moves forward, headrest has to follow to driver''s head, or else if driver push headrest, headrest has to move backward. For this method, we used photo and piezo sensors. The photo sensor was installed to check the distance between head and headrest, and the piezo sensor was installed to check pressure from driver''s head to headrest.

Stricter emission limitations for NOx and particulates in mobile applications will require the use of active aftertreatment methods like Diesel Particulate Filters (DPF), Selective Catalytic Reduction (SCR) with urea and Lean NOx Trap (LNT) as combinations in the 2010's. Due to the significant total space and required investments, a lot of efforts have been focused recently on the optimization of the combinatory aftertreatment systems (ATS). In this study the possibilities to intensify the catalytic ATS were analyzed and reviewed by the examples and studies with engines, laboratory reactors and simulations. The focus was on diesel applications, where the number of needed ATS units is the widest. The diesel engine modifications on SCR or EGR engines have to be also designed together with ATS. The intensification includes the principles of down-sizing and the integration of ATS units with control systems.

A number of the qualitatively new technological process in the electrotechnical installation for processing the life support products are implemented when the mass transfer in the solution is controlled by application of the magnetic fields (for Hartmann numbers Ha = 0…4). Analysis of active zones in the boundary layers, in the artificial turbulent areas in the near membrane zones modelled analytically and numerically allows to formulate principles for designing the intensive and optimal technologies and for selection of the technological modes for the electrochemical system operation. The mathematical model of interaction of the hydrodynamic, electric and magnetic fields in the electrochemical system is considered.

Impending Environmental Protection Agency (EPA) regulations will place severe limits on exhaust emissions of heavy duty diesel engines for urban bus and highway truck applications. To meet this challenge an intensifier-injector system for natural gas fueling of diesel engines is being developed. The intensifier-injector concept employs electronically-controlled, late-cycle, direct injection of high-pressure natural gas with a pilot quantity of diesel fuel. Preliminary performance and emissions data are presented to indicate the potential for diesel engine efficiencies with reduced emissions with this method of natural-gas fueling.

This paper presents the results of computer simulation of thermal and stressed-strained states of steel parts of complicated configuration during quenching. It should be noted that there are a high level of hardware and software developed which is used for computations of thermal and stressed-strained states of steel parts. However, a further progress is restrained by a lack of material properties and heat transfer characteristics databases needed for calculations. Use of intensive steel quenching methods in practice can save millions of dollars and improve environmental conditions. In spite of this no good results of calculations can be achieved without an appropriate database. It is suggested to solve this problem by joining efforts of different companies and scientists. Two companies, the Intensive Technologies Ltd of Kyiv, Ukraine and IQ Technologies Inc of Akron, Ohio have already started this work.

Human drivers navigate by continuously predicting the intent of road users and interacting with them. For safe autonomous driving, research about predicting future trajectory of vehicles and motion planning based on these predictions has drawn attention in recent years. Most of these studies, however, did not take into account driver’s intentions or any interdependence with other vehicles. In order to drive safely in real complex driving situations, it is essential to plan a path based on other driver’s intentions and simultaneously to estimate the intentions of other road user with different characteristics as human drivers do. We aim to tackle the above challenges on highway merge scenario where the intention of other road users should be understood. In this study, we propose an intention aware motion planning method using finite state machine and model predictive control without any vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communications.

Accurate surrounding vehicle motion prediction is critical for enabling safe, high quality autonomous driving decision-making and motion planning. Aiming at the problem that the current deep learning-based trajectory prediction methods are not accurate and effective for extracting the interaction between vehicles and the road environment information, we design a target vehicle intention-aware dual attention network (IDAN), which establishes a multi-task learning framework combining intention network and trajectory prediction network, imposing dual constraints. The intention network generates an intention encoding representing the driver’s intention information. It inputs it into the attention module of the trajectory prediction network to assist the trajectory prediction network to achieve better prediction accuracy.

Traffic accidents avoidance is one of the main advantages for automated vehicles. As one of the main causes of vehicle collision accidents, lane changing of the ego vehicle in case that the obstacle vehicles appear in the blind spot with uncertain motion intentions is one of the main goals for the automated vehicle. An intention-aware lane changing collision assistance strategy basing on traffic situation assessment in the complex traffic scenarios is proposed in this paper. Typical Regions of Interest (ROI) within the detection range of the blind spots are selected basing on the road topology structures and state space consisting of the ego vehicle and the obstacle vehicles. Then the motion intentions of the obstacle vehicles in ROI are identified basing on Gaussian Mixture Models (GMM) and the corresponding motion trajectories are predicted basing on the state equation.

Many fatal vehicle crashes are intentional acts to cover an act of suicide or murder. Some experts estimate that this total can be as large as 10% to 25% of all motor vehicle fatals. The stigma attached to suicide causes families to conceal the details, notes, threats, etc. to authorities. Today's society takes vehicle accidents as a matter of fact and injuries resulting from them. Consequently, a staged crash is not always examined in detail to determine the causal factors which include murder. This is particularly true of single vehicle crashes in which the driver, the only occupant, is dead. Police may assume that since the deceased brought about their own demise that no additional investigation is necessary. The unfortunate result of reporting these incidents as “accidents” results in inappropriate insurance payouts which affect rates and inflate the fatal statistics that are used to enact vehicle regulations. The latter can increase the manufacturering costs of vehicles.

A vehicle's gasoline fuel tank was removed and replaced with a 5,000-psig, Type-III, aluminum-lined hydrogen cylinder. High-pressure cylinders are typically installed with a thermally-activated pressure relief device (PRD) designed to safely vent the contents of the cylinder in the event of accidental exposure to fire. The objective of this research was to assess the results of a catastrophic failure in the event that a PRD were ineffective. Therefore, no PRD was installed on the vehicle to ensure cylinder failure would occur. The cylinder was pressurized and exposed to a propane bonfire in order to simulate the occurrence of a gasoline pool fire on the underside of the vehicle. Measurements included temperature and carbon monoxide concentration inside the passenger compartment of the vehicle to evaluate tenability. Measurements on the exterior of the vehicle included blast wave pressures. Documentation included standard, infrared, and high-speed video.

Previous study on the KIV model proved that it is feasible to extract information from the amygdala for navigation tasks. In this work, we observed the chaotic dynamics of the KIV model by analyzing the global phase transitions that occur in the amygdala. By using the Hilbert transform, we are able to capture the fast global synchronized spatial patterns of amplitude modulation (AM) in the Amygdala. The ultimate goal for identifying the phase transition within the amygdala is to aid the system for decision-making while navigating in a challenging environment with an intentional behavior. In this paper, we will concentrate on the phase transition analysis in the amygdala of the KIV model. Our experiment has shown the detection of phase transition in the amygdala can be done by Hilbert transform.